Evidence map›Paper›PMID 42539623›Full record

ArticleFrontiers in psychology2026

The impact of elderly digital literacy on subjective wellbeing: a moderated mediation model.

Jihui Liu, Yan Qiao, Yuanyuan Shao, Chuhan Fu, Tongxin Wang, Quanquan Guo, Xuan Zhao

Abstract read
In one paragraph

Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Jihui LiuCollege of Humanities and Social Sciences, Hebei Agricultural University, Baoding, China.
Yan QiaoCollege of Humanities and Social Sciences, Hebei Agricultural University, Baoding, China.
Yuanyuan ShaoCollege of Humanities and Social Sciences, Hebei Agricultural University, Baoding, China.
Chuhan FuCollege of Humanities and Social Sciences, Hebei Agricultural University, Baoding, China.
Tongxin WangCollege of Humanities and Social Sciences, Hebei Agricultural University, Baoding, China.
Quanquan GuoCollege of Humanities and Social Sciences, Hebei Agricultural University, Baoding, China.
Xuan ZhaoCollege of Humanities and Social Sciences, Hebei Agricultural University, Baoding, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: This study investigates how digital literacy influences subjective wellbeing among older adults, focusing on the mediating role of self-efficacy and the moderating role of acceptance of artificial intelligence (AI)-based elderly care services. The aim is to provide empirical evidence for enhancing older adults' subjective wellbeing and optimizing intelligent elderly care services. Methods: A survey was conducted with 425 older adults using validated scales measuring digital literacy, self-efficacy, acceptance of AI-based elderly care services, and subjective wellbeing. Data were analyzed using SPSS and Mplus, including descriptive statistics, correlation analysis, mediation tests, moderation tests, and moderated mediation analysis. Results: The results showed that digital literacy positively affected subjective wellbeing ( Discussion: These findings suggest that in an aging society, efforts should be made to enhance older adults' digital literacy and foster positive acceptance of AI-based elderly care services, thereby improving subjective wellbeing through increased self-efficacy. This study provides a reference for the formulation of intelligent elderly care policies.

Indexed as

acceptance of artificial intelligence-based elderly care servicesdigital literacymoderated mediation modelself-efficacysubjective wellbeing

Identifiers

PMID42539623
PMCPMC13424191

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.